BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//aichemy - ECPv6.17.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:aichemy
X-ORIGINAL-URL:https://aichemy.ac.uk
X-WR-CALDESC:Events for aichemy
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20250101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20260218T140000
DTEND;TZID=UTC:20260218T150000
DTSTAMP:20260128T132849Z
CREATED:20260128T132849Z
LAST-MODIFIED:20260128T132849Z
UID:12212-1771423200-1771426800@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series - February 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE18 February 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nOrganometallic Chemistry x Data Science Rethinking Generative AI for Materials Discovery \n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nProf. Natalie Fey – University of Bristol \n\n\n\nTalk Title: Organometallic Chemistry x Data ScienceComputational studies of homogeneous catalysis play an increasingly important role in furthering (and changing) our understanding of catalytic cycles and can help to guide the discovery and evaluation of new organometallic catalysts. While a truly “rational design” process often remains out of reach\, detailed mechanistic information from both experiment and computation can be combined successfully with suitable parameters characterising catalysts and substrates to predict outcomes and guide screening. \n\n\n\nIn this presentation\, I will use examples drawn from our recent work\, including the exploration of maps of chemical space and of a reactivity database\, to illustrate how we are increasingly applying data science techniques for visualisation and prediction\, with the goal of informing the discovery and design of suitable organometallic catalysts. \n\n\n\nHyunsoo Park – Imperial College LondonTalk Title: Rethinking Generative AI for Materials Discovery \n\n\n\nGenerative artificial intelligence (AI) has emerged as a potent paradigm for inverse materials design\, offering the potential to invert traditional discovery workflows by directly proposing structures that satisfy desired properties. However\, a fundamental challenge persists regarding the standard training objectives used in generative AI versus the goals of materials discovery. A critical misalignment exists between the likelihood-based sampling typical of generative modelling and the targeted focus on underexplored regions required to identify novel compounds. \n\n\n\nTo address this challenge\, this talk presents Chemeleon2\, a framework that reformulates crystal generation as a reinforcement learning (RL) task. Through the integration of Group Relative Policy Optimization (GRPO) with latent diffusion models\, the system optimizes multi-objective rewards to simultaneously achieve stability\, diversity\, and novelty. The presentation further details how this methodology facilitates property-guided design\, ensuring chemical validity while isolating desired functionalities. Ultimately\, this approach establishes a modular foundation for controllable\, AI-driven inverse design\, effectively addressing the novelty-validity trade-off inherent in scientific discovery applications. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Natalie FeyProfessor of Chemistry\n\n\n\n\n\nHyunsoo Park Research Associate in Materials Informatics\n\n\n\n\n\nDr. Adam ClaytonWebinar HostAssociate Professor
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-february-2026-2/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/01/AIchemy-Webinar-Feb-26.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260311T000000
DTEND;TZID=UTC:20260415T235959
DTSTAMP:20260209T105627Z
CREATED:20260209T105627Z
LAST-MODIFIED:20260209T105627Z
UID:12224-1773187200-1776297599@aichemy.ac.uk
SUMMARY:Patenting AI & Materials: IP Webinars
DESCRIPTION:KEY DETAILS\n\n\n\n\nWEBINAR 1: IP FUNDAMENTALS DATE & TIME11 march 2026\, 14:00 – 15:00 WEBINAR 2: STRATEGIC IP DATE & TIME15 APRIL 2026\, 14:00 – 15:00 \n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nWEBINAR 1: IP FUNDAMENTALS WEBINAR 2: STRATEGIC IP \n\n\n\n\n\n\n\n\nJoin Keltie LLP patent attorneys Dr Monica Patel and Dr Emily Weal for a two-part webinar series on protecting innovation at the intersection of AI and materials science. The sessions will guide researchers\, innovators and start-ups through IP fundamentals\, patenting strategies\, and practical tools for recognising and protecting commercially valuable ideas in AI-enabled materials discovery. \n\n\n\nWEBINAR 1: IP FUNDAMENTALS \n\n\n\nThis webinar will introduce the fundamentals of IP for researchers and innovators working at the intersection of AI and materials. The session will cover the differences between patents\, registered designs and trade marks\, how the patent process works in the UK and internationally\, and what typical hurdles to patentability look like in practice. The session will showcase real examples of patentable technologies in materials science and AI\, and highlight how AI-driven approaches are being applied to materials discovery and development. The webinar is designed for a broad audience\, and no prior knowledge of IP or patents is required. \n\n\n\nWEBINAR 2: STRATEGIC IP \n\n\n\nThis webinar will build on these foundations to focus on how to recognise and protect commercially valuable ideas in AI and materials. The session will cover how to identify patentable inventions in your research\, principles of strategic patent drafting for data-driven and AI-enabled materials innovations\, and common IP ownership and collaboration pitfalls in multi-partner projects. The session will also cover an introduction to competitor patent searching and patent landscaping techniques\, and practical IP tips tailored for start-ups and spin-outs emerging from the AI and materials ecosystem. While open to all\, attendees will benefit from having joined Webinar 1 or having a basic familiarity with core IP concepts. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nDr Monica PatelSenior Associate\, Keltie LLP\n\n\n\n\n\nDr Emily WealPartner\, Keltie LLP
URL:https://aichemy.ac.uk/event/patenting-ai-materials-ip-webinars-keltiellp-2/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/02/IP-Webinars.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260318T140000
DTEND;TZID=UTC:20260318T150000
DTSTAMP:20260325T144026Z
CREATED:20260209T224922Z
LAST-MODIFIED:20260325T144026Z
UID:7477-1773842400-1773846000@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – March 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE18 March 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nResolving the data ambiguity for periodic crystals The Crystal Geomap visualises materials databases in real time. \n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nProf. Vitaliy Kurlin – University of Liverpool \n\n\n\nTalk Title: Resolving the data ambiguity for periodic crystalsThe discontinuity of cell-based representations of periodic crystals under almost any noise has been known theoretically and experimentally at least since 1965. As a result\, major materials databases accumulated thousands of near-duplicate structures that could not be recognized by any past tools [1]. The latest example is the correction of the A-lab paper in Nature [2]\, where almost all words “novel” and “discovery” were crossed out. We will present a rigorously justified approach to uniquely identifying the atomic structure of any periodic crystal by complete\, continuous and fast geometric codes [3]. \n\n\n\n[1] D.Chawla. C&EN news\, https://cen.acs.org/research-integrity/Duplicate-structures-haunt-crystallography-databases/103/web/2025/12. \n\n\n\n[2] N.Szymanski et al. Author Correction: An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature (2026)\, https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-025-09992-y/MediaObjects/41586_2025_9992_MOESM1_ESM.pdf \n\n\n\n[3] D.Widdowson\, V.Kurlin. Resolving the data ambiguity for periodic crystals. NeurIPS 2022\, v.35\, p.24625-2463. Extended version to appear in SIAM J Appl. Math. 2026\, https://arxiv.org/abs/2108.04798. \n\n\n\nDr. Daniel Widdowson  – University of Liverpool \n\n\n\nTalk Title: The Crystal Geomap visualises materials databases in real time \n\n\n\nOur rigorously justified invariants of crystals give rise to a continuous space containing all crystals\, where the proximity of two crystals does not depend on a choice of unit cell and motif\, but whether the two structures can be closely matched atom for atom by isometry [4]. This led us to develop software to visualise this space and be an interface to ultra-fast comparisons of crystals enabled by our invariants [5]. In this talk we will explore unusual “features” of crystal databases such as the ICSD made visible by our depictions of crystal space\, examples of nearly identical crystals represented with completely different cells and motifs [6]\, and a live example of detection of all geometric (near-)duplicates in the ICSD\, a calculation which was computationally intractable by existing methods. \n\n\n\n[4] O.Anosova\, V.Kurlin\, M.Senechal. The importance of definitions in crystallography. IUCrJ\, v.11(4)\, p.453-463 (2024). \n\n\n\n[5] D.Widdowson\, V.Kurlin. Continuous invariant-based maps of the Cambridge Structural Database. Crystal Growth & Design\, v.24(13)\, p.5627–5636 (2024). \n\n\n\n[6] D.Widdowson\, V.Kurlin. Geographic-style maps with a local novelty distance help navigate the materials space. Scientific Reports\, v.15\, 27588 (2025). \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Vitaliy KurlinProfessor of Computer Science\n\n\n\n\n\nDr. Daniel Widdowson Senior Software Engineer\n\n\n\n\n\nDr. John Ward – Webinar Chair Lecturer in Chemistry
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-march-2026/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/02/Aichemy-Webinar-Mar-2026.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260422T140000
DTEND;TZID=UTC:20260422T150000
DTSTAMP:20260818T110355Z
CREATED:20260325T142551Z
LAST-MODIFIED:20260818T110355Z
UID:12248-1776866400-1776870000@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – April 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE22nd April 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nAI-Driven Experiments and Open-Source Automation for Accelerated Soft Matter Research Developing sustainable separation processes with AI \n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nProf. Lilo D. Pozzo – University of Washington \n\n\n\nTalk Title: AI-Driven Experiments and Open-Source Automation for Accelerated Soft Matter Research \n\n\n\nArtificial intelligence (AI)\, when paired with accessible laboratory automation\, can greatly accelerate materials optimization and scientific discovery. For example\, it can be used to efficiently map a phase-diagram with intelligent sampling along phase boundaries\, or in ‘retrosynthesis’ problems where a material with a target structure is desired but a synthetic route is not known. These approaches are especially promising in soft matter systems\, including block copolymer self-assembly\, nanoparticle synthesis\, and controlled colloidal assembly. In these systems\, design parameters (e.g. chemical composition\, MW\, topology\, processing) are vast\, history-dependent metastable and ‘out-of-equilibrium’ structures are common\, and functional properties are intimately tied to molecular design features and processing conditions. In addition\, for AI algorithms to operate efficiently in these spaces\, they must be ‘encoded’ with domain expertise specific to the problems being tackled. This talk will cover recent advances in accelerated materials research involving polymeric and soft-matter systems including dispersions and colloids. It will also outline remaining challenges and future opportunities. \n\n\n\nShort Biosketch: \n\n\n\nProf. Pozzo’s research interests are in the area of colloids\, polymers and soft-matter systems. Her research group focuses on controlling and manipulating materials structure for applications in healthcare\, alternative energy and sustainability. Her group also develops and utilizes laboratory automation and artificial intelligence (AI) to accelerate the development time-scales of new materials and applies advanced techniques based on neutron and x-ray scattering to characterize their nanostructure. Prof. Pozzo obtained her B.S. from the University of Puerto Rico at Mayagüez and her PhD in Chemical Engineering from Carnegie Mellon University in Pittsburgh PA. She also worked at the NIST Center for Neutron Research as a post-doctoral fellow and is currently the Boeing-Roundhill Chair Professor of Chemical Engineering at the University of Washington where she has served since 2007. She has been recognized with awards such as the Early Career Award from the Department of Energy\, the Clean Energy Empowerment and Education Award (C3E) from DOE\, and the Anne Mayes Award from the Neutron Scattering Society of America (NSSA). In addition to her research activities\, she is also dedicated to improving engineering education with course development in areas of entrepreneurship and service-oriented global engagement. \n\n\n\nJiyizhe Zhang – The University of Manchester \n\n\n\nTalk Title: Developing sustainable separation processes with AI \n\n\n\nChemical separations have long been essential to human society\, yet the separation of complex mixtures often remains lengthy and costly. Liquid-liquid extraction\, as a separation technology\, has wide applications in pharmaceuticals\, bioprocessing\, critical mineral recovery\, and nuclear waste treatment. Despite its widespread use\, many of the underlying physicochemical phenomena in liquid-liquid systems are not fully understood\, and the process development still relies heavily on shake-flask experiments as decades ago. This talk will present emerging technologies to accelerate separation process development through artificial intelligence\, automation\, and process modelling. Key challenges and future opportunities for digitalising separation science will be discussed. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Lilo D. Pozzo Professor of Chemical Engineering\n\n\n\n\n\nJiyizhe Zhang Lecturer in Chemical Engineering\n\n\n\n\n\nTahereh Nematiaram – Webinar Chair Chancellor’s Fellow\n\n\n\n\n\n\n\n\n\n\n\nSpeaker Nominations\n\n\n\nWe welcome suggestions from the community for both our main speaker talks and Early Career Researcher talks (ECR – defined as late-stage PhD or postdocs). The aim of these webinars is to cover a range of topics in digital chemistry\, including general purpose robotic systems\, high-throughput automation\, closed-loop and human-in-the-loop workflows\, generative AI\, multi-fidelity AI\, reinforcement learning\, and optimisation (this is not an exhaustive list).Please fill out the form below to suggest or nominate potential speakers. Self-nominations are also encouraged. \n\n\n\nNominate a speaker
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-april-2026-2/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/03/April-Webinar-2026-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260520T140000
DTEND;TZID=UTC:20260520T150000
DTSTAMP:20260818T112222Z
CREATED:20260508T151957Z
LAST-MODIFIED:20260818T112222Z
UID:9410-1779285600-1779289200@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – May 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE20th May 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\n FLIP: Flowability-Informed Powder Weighing \n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nProf. Bao Nguyen – University of Leeds \n\n\n\nTalk Title: Who’s learning from whom? Beyond the black boxes of chemical models. \n\n\n\nArtificial intelligence and machine learning are now central tools for chemists seeking to predict molecular properties and reaction outcomes. Yet as these models grow increasingly sophisticated\, their inner workings often remain opaque\, and the chemical data they rely on—like all experimental data—can be noisy\, sparse\, or biased. In this talk\, Bao will illustrate how we address these challenges in the context of solubility prediction: from handling imperfect datasets to building models that both perform robustly and provide trustworthy predictions on previously unseen data. \n\n\n\nHe will then show how the usual paradigm can be reversed. Rather than using algorithms solely to predict the results of complex reactions\, we can use the data generated through Bayesian Optimisation to reveal mechanistic insights that would otherwise remain hidden. This shift—from prediction to understanding—opens new opportunities for rationally tackling selectivity problems in modern synthetic chemistry. \n\n\n\nNikola Radulov – University of Liverpool \n\n\n\nTalk Title: FLIP: Flowability-Informed Powder Weighing \n\n\n\nAutonomous manipulation of powders remains a significant challenge for robotic automation in scientific laboratories. The inherent variability and complex physical interactions of powders in flow\, coupled with variability in laboratory conditions necessitates adaptive automation. We introduce FLIP\, a flowability-informed powder weighing framework designed to enhance robotic policy learning for granular material handling. The core of the framework lies in using material flowability\, quantified by the angle of repose\, to optimise physics-based simulations through Bayesian inference. This yields material-specific simulation environments capable of generating accurate training data\, which reflects diverse powder behaviours\, for training “robot chemists”.  We demonstrate how FLIP integrates quantified flowability into a curriculum learning strategy\, fostering efficient acquisition of robust robotic policies by gradually introducing more challenging\, less flowable powders. We validate the efficacy of our method on a robotic powder weighing task under real-world laboratory conditions. Experimental results show that FLIP with a curriculum strategy achieves a low dispensing error of 2.12 +/- 1.53 mg\, outperforming methods that do not leverage flowability data\, such as domain randomisation (6.11 +/- 3.92 mg). These results demonstrate FLIP’s improved ability to generalise to previously unseen\, more cohesive powders and to new target masses.Following the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Bao Nguyen Physical Organic Chemistry\n\n\n\n\n\nNikola RadulovEarly Career Research\n\n\n\n\n\nDr. Adam ClaytonAssociate Professor \n\n\n\n\n\n\n\n\n\n\n\nSpeaker Nominations\n\n\n\nWe welcome suggestions from the community for both our main speaker talks and Early Career Researcher talks (ECR – defined as late-stage PhD or postdocs). The aim of these webinars is to cover a range of topics in digital chemistry\, including general purpose robotic systems\, high-throughput automation\, closed-loop and human-in-the-loop workflows\, generative AI\, multi-fidelity AI\, reinforcement learning\, and optimisation (this is not an exhaustive list).Please fill out the form below to suggest or nominate potential speakers. Self-nominations are also encouraged. \n\n\n\nNominate a speaker
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-may-2026/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/jpeg:https://aichemy.ac.uk/wp-content/uploads/2026/05/May-Webinar-2026.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260617T140000
DTEND;TZID=UTC:20260617T150000
DTSTAMP:20260818T110613Z
CREATED:20260601T105940Z
LAST-MODIFIED:20260818T110613Z
UID:12196-1781704800-1781708400@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – June 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE17th June 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nCombinatorial explosion: from atom-bond arrangements to exotic diseases Low-cost mechanism-informed features enable transferable enantioselectivity predictions from sparse data \n\n\n\n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nAssoc. Prof. Timothy Cernak – University of Michigan \n\n\n\nTalk Title: Combinatorial explosion: from atom-bond arrangements to exotic diseases \n\n\n\nChemical synthesis and data science are two fields that operate in synergy. Molecules and the routes to synthesize them are easily represented as graphs while automated chemical synthesis strategies allow more and more synthesis data to be captured\, for instance to feed machine learning algorithms. This talk will detail our work in this area focused on a new class of amine-acid cross coupling reactions\, and the computer-assisted synthesis of drugs and natural products. We have been exploring the breadth of all reactions that could exist\, navigating combinatorial explosions of virtual and plausible reaction methods\, routes to complex molecules\, and the interconnectedness of reaction conditions\, transformations\, and biological functions. \n\n\n\nOur agnostic view of reactions and their mechanisms has recently extended to diseases\, with a focus on One Health. We aspire to produce medicines and treatments for health challenges in endangered species. We call this new area conservation chemistry\, and examples from the frontlines of this field and lab-based research will be shared. \n\n\n\nDr. Simone Gallarati – University of Utah \n\n\n\nTalk Title: Low-cost mechanism-informed features enable transferable enantioselectivity predictions from sparse data \n\n\n\nIn order to optimize an asymmetric reaction\, machine learning (ML) models are frequently implemented to screen virtual libraries of chiral catalysts and identify candidates with superior performance. Unfortunately\, such models are often poorly transferable to new reactions involving a different combination of known substrate types or an entirely unfamiliar class of compounds. In this talk\, I will first introduce a descriptor generation strategy that accounts for possible changes in a reaction’s stereodetermining step with catalyst or substrate identity\, allowing us to model mechanistically complex transformations involving distinct ligand and substrate types. Our ML workflow has led to the optimization of poorly performing examples reported in a substrate scope and to accurate out-of-sample predictions on unseen ligand and reaction partners.1 \n\n\n\nOne limitation of inference-based ML models is the need for large virtual libraries of potential catalysts\, whose curation is frequently associated with significant computational costs. In the second part of the talk\, I will introduce a genetic algorithm-based pipeline2 whereby only a small population of ligands is computed and evaluated experimentally at each iteration of the optimization loop. This strategy leverages the modularity of catalyst scaffolds and is compatible with early reaction optimization campaigns\, requiring the featurization and synthesis of only small batches of ligands. Overall\, these workflows enable streamlined reaction development\, quantitatively transferring knowledge learned on sparse data sets to novel chemical spaces. \n\n\n\nReferences \n\n\n\n(1) Gallarati\, S.; Bucci\, E. M.; Doyle\, A. G.; Sigman\, M. S. Transferable Enantioselectivity Models from Sparse Data. Nature 2026\, 651\, 637–646. \n\n\n\n(2) Gallarati\, S.; van Gerwen\, P.; Schoepfer\, A. A.; Laplaza\, R.; Corminboeuf\, C. Genetic Algorithms for the Discovery of Homogeneous Catalysts. CHIMIA 2023\, 77 (1/2)\, 39.Following the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nAssoc. Prof. Timonthy CernakMedicinal Chemistry\n\n\n\n\n\nDr.Simone Gallarati Postdoctoral researcher\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker Nominations\n\n\n\nWe welcome suggestions from the community for both our main speaker talks and Early Career Researcher talks (ECR – defined as late-stage PhD or postdocs). The aim of these webinars is to cover a range of topics in digital chemistry\, including general purpose robotic systems\, high-throughput automation\, closed-loop and human-in-the-loop workflows\, generative AI\, multi-fidelity AI\, reinforcement learning\, and optimisation (this is not an exhaustive list).Please fill out the form below to suggest or nominate potential speakers. Self-nominations are also encouraged. \n\n\n\nNominate a speaker
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-june-2026-2/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/06/June-Webinar-26-1.png
END:VEVENT
END:VCALENDAR